Fuel load analysis with Google Earth Engine (GEE) and Python Engine (PE) in forest fire management: Challenges and benefits

Abstract

Fuel load is one of the fundamental factors determining forest fire behavior. Dynamics such as fire intensity, flame length, and fuel consumption are directly related to this parameter. However, while traditional field measurements provide detailed data, repeating these processes over large areas leads to high costs and time loss. Remote sensing (RS) technologies overcome these limitations by enabling rapid and effective fuel load estimation over wide regions. In this study, the fuel load dynamics before and after the 2021 Manavgat megafire were analyzed using the cloud-based Google Earth Engine (GEE) platform and Sentinel-2 multispectral imagery. Maps of surface fuel load were generated in accordance with the Rothermel model by utilizing the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR); median composites were created from time-series data with cloud coverage below 20%. Descriptive statistics and meta-analysis techniques were applied using Python in the Google Colab environment to ensure data consistency. NDVI and NBR values at sampling points were classified into four groups to directly support fire behavior models. These values were grouped by percentile classification as “Low” (< 20%), “Moderate” (20–50%), “High” (50–80%), and “Very High” (> 80%), providing direct input to the Rothermel equation parameters. Additionally, to assess pre-fire drought dynamics, analyses of the Vegetation Condition Index (VCI) and the Vegetation Drought Severity Index (SDVI) were conducted. The results demonstrate that this integrated spatial approach offers strong predictive power for decision-makers in pre-fire intervention planning.

Keywords

• Forest
• Ecology
• GIS
• GEE
• Fuel Load Map

Orman yangını yönetiminde Google Earth Engine (GEE) ve Python Engine (PE) ile yakıt yükü analizi: Karşılaşılan güçlükler ve sağlanan avantajlar

Öz

Yakıt yükü, orman yangını davranışını belirleyen temel unsurlardan biridir. Yangının şiddeti, alev uzunluğu ve yakıt tüketimi gibi dinamikler doğrudan bu parametreye bağlıdır. Ancak geleneksel saha ölçümleri büyük alanlarda tekrarlandığında yüksek maliyet ve zaman kaybı yaratmaktadır. Uzaktan algılama (UA) teknolojileri bu sınırlamaları aşarak geniş alanlarda hızlı ve etkin yakıt yükü tahmini imkânı sunmaktadır. Bu çalışmada, 2021’de Manavgat’ta meydana gelen mega yangın örneği üzerinden Google Earth Engine (GEE) platformu ve Sentinel-2 multispektral görüntüleri kullanılarak yangın öncesi ve sonrası yakıt yükü davranışı analiz edilmiştir. Normalize Fark Bitki Örtüsü (NDVI) ve Normalize Yanma Oranı (NBR) indeksleri kullanılarak, Rothermel modeline uygun yüzey yakıt yükü haritaları oluşturulmuş; bulut örtüsü %20’nin altında olan görüntülerden zaman serisi bazlı medyan kompozitler elde edilmiştir. Google Colab ortamında Python diliyle yürütülen analizlerde betimleyici istatistikler ve meta-analiz yöntemleriyle veri tutarlılığı sağlanmıştır. Örnekleme yöntemiyle belirlenen noktalardaki NDVI ve NBR değerleri dört sınıfa ayrılmış ve yangın davranışı modellerine doğrudan veri sağlamıştır. NDVI ve NBR değerleri, persentile sınıflandırmasıyla “Düşük” (< %20), “Orta” (%20–50), “Yüksek” (%50–80) ve “Çok Yüksek” (> %80) olarak gruplandırılmış; bu ayrım Rothermel denkleminin parametrelerine doğrudan girdi sağlamıştır. Ayrıca yangın öncesi kuraklık dinamiklerini incelemek amacıyla Bitki Örtüsü Durumu Endeksi (VCI) ve Bitki Örtüsü Kuraklık Şiddeti İndeksi (SDVI) analizleri yapılmıştır. Sonuçlar, bu mekânsal yaklaşımın yangın öncesi müdahale planlamasında karar vericilere güçlü bir öngörü sunduğunu göstermektedir.

Anahtar Kelimeler

• Ekoloji
• Orman
• Coğrafi Bilgi Sistemleri
• Google Earth Engine
• Yakın Yükü Haritası

Kaynakça

1. Abdollahi, A. & Yebra, M. (2025). Challenges and opportunities in remote sensing-based fuel load estimation for wildfire behavior and management: A comprehensive review. Remote Sensing, 17(3), 415. https://doi.org/10.3390/rs17030415.
2. Adab, H., Kanniah, K. D. & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3), 1723-1743. https://doi.org/10.1007/s11069-012-0450-8.
3. Agee, J., Wakimoto, R., Darley, E. & Biswell, H. (1973). Eucalyptus fuel dynamics, and fire hazard in the Oakland hills. California Agriculture, 27(9), 13-15.
4. Akıncı, H. A. & Akıncı, H. (2023). Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Science Informatics, 16(1), 397-414. https://doi.org/10.1007/s12145-023-00953-5.
5. Alexander, M. E. & Cruz, M. G. (2006). Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Canadian Journal of Forest Research, 36(11), 3015-3028. https://doi.org/10.1139/x06-174.
6. Andrews, P. L. (2007). BehavePlus fire modeling system: Past, present, and future. In Proceedings of 7th Symposium on Fire and Forest Meteorology. American Meteorological Society. https://research.fs.usda.gov/treesearch/31549.
7. Arellano-Pérez, S., Castedo-Dorado, F., López-Sánchez, C. A., González-Ferreiro, E., Yang, Z., Díaz-Varela, R. A., Álvarez-González, J. G., Vega, J. A. & Ruiz-González, A. D. (2018). Potential of Sentinel-2A data to model surface and canopy fuel characteristics in relation to crown fire hazard. Remote Sensing, 10(10), 1645. https://doi.org/10.3390/rs10101645.
8. Arroyo, L. A., Pascual, C. & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management, 256(6),1239-1252. https://doi.org/10.1016/j.foreco.2008.06.048.

9. Bai, X., He, B., Li, X., Zeng, J., Wang, X., Wang, Z., Zeng, Y. & Su, Z. (2017). First assessment of sentinel-1a data for surface soil moisture estimations using a coupled water cloud model and advanced ıntegral equation model over the Tibetan Plateau. Remote Sensing, 9(7), 714. https://doi.org/10.3390/rs9070714.
10. Bosela, M., Larocque, G. R., Baycheva, T., Valbuena, R. & Lier, M. (2024). Criteria and ındicators of sustainable forest management. In G. R. Larocque (Ed.), Ecological forest management handbook (2nd ed., pp. 356-385). CRC Press.
11. Bowman, D. M. J. S., Kolden, C. A., Abatzoglou, J. T., Johnston, F. H., van der Werf, G. R. & Flannigan, M. (2020). Vegetation fires in the Anthropocene. Nature Reviews Earth & Environment, 1(10), 500-515. https://doi.org/10.1038/s43017-020-0085-3.
12. Bowman, D. M. J. S., Moreira-Muñoz, A., Kolden, C. A., Chávez, R. O., Muñoz, A. A., Salinas, F., González-Reyes, Á., Rocco, R., de la Barrera, F., Williamson, G. J., Borchers, N., Cifuentes, L. A., Abatzoglou, J. T. & Johnston, F. H. (2019). Human–environmental drivers and impacts of the globally extreme 2017 Chilean fires. Ambio, 48(4), 350-362. https://doi.org/10.1007/s13280-018-1084-1.
13. Bresnehan, S. J. (2003). An assessment of fuel characteristics and fuel loads in the dry sclerophyll forests of South-East Tasmania [Unpublished doctoral dissertation]. University of Tasmania. https://doi.org/10.25959/23207204.v1.
14. Carmona-Moreno, C., Belward, A., Malingreau, J.-P., Hartley, A., Garcia-Alegre, M., Antonovskiy M., Buchshtaber, V. & Pivovarov, V. (2005). Characterizing interannual variations in global fire calendar using data from earth observing satellites. Global Change Biology, 11(9), 1537-1555.
15. Chen, Y., Zhu, X., Yebra, M., Harris, S. & Tapper, N. (2017). Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data. Environmental Modelling & Software, 97, 61-71. https://doi.org/10.1016/j.envsoft.2017.07.007.
16. Chuvieco, E., Riaño, D., Van Wagtendok, J. & Morsdof, F. (2003). Fuel loads and fuel type mapping. In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping: The role of Remote sensing data (pp. 119-142). World Scıentıfıc. https://doi.org/10.1142/9789812791177_0005.
17. Colvard, N. B., Watson, C. E. & Park, H. (2018). The ımpact of open educational resources on various student success metrics. International Journal of Teaching and Learning in Higher Education, 30(2), 262-276.
18. Countryman, C. M. (1972). The fire environment concept. Pacific Southwest Forest and Range Experiment Station.
19. Creswell, J.W. & Plano Clark. (2017). Designing and conducting mixed methods research. Sage Publications.
20. Cruz, M. G., Alexander, M. E. & Wakimoto, R. H. (2003). Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39-50. https://doi.org/10.1071/WF02024.
21. Datta, R. (2021). To extinguish or not to extinguish: The role of forest fire in nature and soil resilience. Journal of King Saud University Science, 33(6), 101539. https://doi.org/10.1016/j.jksus.2021.101539.
22. Dawadi, S., Shrestha, S. & Giri, R. A. (2021). Mixed-methods research: A discussion on its types, challenges, and criticisms. Journal of Practical Studies in Education, 2(2), 25-36. https://doi.org/10.46809/jpse.v2i2.20.
23. Díaz, S. M., Settele, J., Brondízio, E., Ngo, H., Guèze, M., Agard, J., Arneth, A., Balvanera, P., Brauman, K., Butchart, S., Chan, K. M. A., Garibaldi, L. A., Ichii, K., Liu, J., Subramanian, S., Midgley, G., Miloslavich, P., Molnár, Z., Obura, D. & Zayas, C. (2019). The global assessment report on biodiversity and ecosystem services: Summary for policy makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://www.ipbes.net/system/files/2021-06/2020%20IPBES%20GLOBAL%20REPORT(FIRST%20PART)_V3_SINGLE.pdf
24. Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B. & Howard, S. (2007). A project for monitoring trends in burn severity. Fire Ecology, 3(1), 3-21. https://doi.org/10.4996/fireecology.0301003.
25. European Environment Agency (2018). Corine land cover [ Data set]. Copernicus Programme. https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac.
26. Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T. & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2), 129-146. https://doi.org/10.1016/j.foreco.2005.06.013.
27. Filipponi, F. (2019). Exploitation of Sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sensing, 11(6), https://doi.org/10.3390/rs11060622.
28. Finney, M. A. (1998). FARSITE, Fire area simulator—model development and evaluation (No. 4). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
29. Flannigan, M. D., Stocks, B. J. & Wotton, B. M. (2000). Climate change and forest fires. Science of The Total Environment, 262(3), 221-229. https://doi.org/10.1016/S0048-9697(00)00524-6.
30. Fontán-Vela, M., Rivera-Navarro, J., Gullón, P., Díez, J., Anguelovski, I. & Franco, M. (2021). Active use and perceptions of parks as urban assets for physical activity: A mixed-methods study. Health & Place, 71, 102660. https://doi.org/10.1016/j.healthplace.2021.102660.
31. Forkuor, G., Benewinde Zoungrana, J.-B., Dimobe, K., Ouattara, B., Vadrevu, K. P. & Tondoh, J. E. (2020). Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets A case study. Remote Sensing of Environment, 236, 111496. https://doi.org/10.1016/j.rse.2019.111496.
32. Gilroy, J. & Tran, C. (2020). A new fuel load model for eucalypt forests in southeast Queensland. The Proceedings of the Royal Society of Queensland, 115, 137-143. https://doi.org/10.3316/ielapa.591547606338230.
33. Gould, J. S., Lachlan McCaw, W. & Phillip Cheney, N. (2011). Quantifying fine fuel dynamics and structure in dry eucalypt forest (Eucalyptus marginata) in Western Australia for fire management. Forest Ecology and Management, 262(3), 531-546. https://doi.org/10.1016/j.foreco.2011.04.022.
34. Güney, C. O., Özkan, K. & Şentürk, Ö. (2015). Antalya-Manavgat yöresi ormanlarında tutuşma riskinin coğrafi dağılım modellemesi. İstanbul Üniversitesi Orman Fakültesi Dergisi, 66(2), 459-470. https://doi.org/10.17099/jffiu.42696.
35. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O. & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853. https://doi.org/10.1126/science.1244693.
36. Ikahihifo, T. K., Spring, K. J., Rosecrans, J. & Watson, J. (2017). Assessing the savings from open educational resources on student academic goals. International Review of Research in Open and Distributed Learning, 18(7), 126-140. https://doi.org/10.19173/irrodl.v18i7.2754.
37. Ivanova, G. A., Kukavskaya, E. A., Ivanov, V. A., Conard, S. G. & McRae, D. J. (2020). Fuel characteristics, loads and consumption in Scots pine forests of central Siberia. Journal of Forestry Research, 31(6), 2507-2524. https://doi.org/10.1007/s11676-019-01038-0.
38. Jaiswal, R. K., Mukherjee, S., Raju, K. D. & Saxena, R. (2002). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1), 1-10. https://doi.org/10.1016/S0303-2434(02)00006-5.
39. Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J., Williamson, G. J. & Bowman, D. M. J. S. (2015). Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications, 6(1), 7537. https://doi.org/10.1038/ncomms8537.
40. Jurskis, V. (2005). Decline of eucalypt forests as a consequence of unnatural fire regimes. Australian Forestry, 68(4), 257-262. https://doi.org/10.1080/00049158.2005.10674974.
41. Kaur, A., Gulati, S., Sharma, R., Sinhababu, A. & Chakravarty, R. (2022). Visual citation navigation of open education resources using Litmaps. Library Hi Tech News, 39(5), 7-11. https://doi.org/10.1108/LHTN-01-2022-0012.
42. Kavgacı, A., Örtel, E., Torres, I. & Safford, H. (2016). Early postfire vegetation recovery of Pinus brutia forests: Effects of fire severity, prefire stand age, and aspect. Turkish Journal of Agriculture and Forestry, 40(5), 723-736.
43. Kaya, B. (2024). Manavgat çayı havzasında vejetasyon devresi ve bitki yetişme şartlarından sıcaklık ve yağış özellikleri. Journal of Turkish Studies, 13(10), 457-486. https://doi.org/10.7827/TurkishStudies.13183.
44. Keane, R. E. (2013). Describing wildland surface fuel loading for fire management: A review of approaches, methods and systems. International Journal of Wildland Fire, 22(1), 51-62. https://doi.org/10.1071/WF11139.
45. Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116-126. https://doi.org/10.1071/WF07049.
46. Keifer, M., van Wagtendonk, J. W. & Buhler, M. (2006). Long-term surface fuel accumulation in burned and unburned mixed-conifer forests of the Central and Southern Sierra Nevada, CA (USA). Fire Ecology, 2(1), 53-72. https://doi.org/10.4996/fireecology.0201053.
47. Kim, S. S., Prasad, A., Nayak, M. M., Chen, H., Srisoem, C., DeMarco, R. F., Castaldi, P. & Cooley, M. E. (2024). Predictors of nicotine replacement therapy adherence: Mixed-methods research with a convergent parallel design. Annals of Behavioral Medicine, 58(4), 275-285. https://doi.org/10.1093/abm/kaae006.
48. Laurance, W. F., Camargo, J. L. C., Luizão, R. C. C., Laurance, S. G., Pimm, S. L., Bruna, E. M., Stouffer, P. C., Bruce Williamson, G., Benítez-Malvido, J., Vasconcelos, H. L., Van Houtan, K. S., Zartman, C. E., Boyle, S. A., Didham, R. K., Andrade, A. & Lovejoy, T. E. (2011). The fate of Amazonian forest fragments: A 32-year investigation. Biological Conservation, 144(1), 56-67. https://doi.org/10.1016/j.biocon.2010.09.021.
49. Lopes Queiroz, G., McDermid, G. J., Castilla, G., Linke, J. & Rahman, M. M. (2019). Mapping coarse woody debris with random forest classification of centimetric aerial ımagery. Forests, 10(6), 471. https://doi.org/10.3390/f10060471.
50. Lovejoy, T.E., Bierregaard, R. O., Rylands, A. B., Malcolm, J.R., Quintela, C., Harper, L., Brown, K., Powell, A., Powell, G., Schubart, H. & Hays, M. (1986). Edge and other effects of isolation on Amazon forest fragments. In M. E. Soule (Ed.), Conservation biology:The science of scarcity and diversity (pp. 257-285). Sinauer Associates.
51. Massetti, A., Rüdiger, C., Yebra, M. & Hilton, J. (2019). The vegetation structure perpendicular ındex (VSPI): A forest condition index for wildfire predictions. Remote Sensing of Environment, 224, 167-181. https://doi.org/10.1016/j.rse.2019.02.004.
52. McKinley, D. C., Ryan, M. G., Birdsey, R. A., Giardina, C. P., Harmon, M. E., Heath, L. S., Houghton, R. A., Jackson, R. B., Morrison, J. F., Murray, B. C., Pataki, D. E. & Skog, K. E. (2011). A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 21(6), 1902-1924. https://doi.org/10.1890/10-0697.1.
53. Miller, C. & Urban, D. L. (2000). Connectivity of forest fuels and surface fire regimes. Landscape Ecology, 15(2), 145-154. https://doi.org/10.1023/A:1008181313360.
54. Mooney, H. (1981). Fire regimes and ecosystem properties: Proceedings of the conference: December 11-15, 1978, Honolulu, Hawaii. USDA Forest Service general technical report WO (USA) No. 26. United States Forest Service. https://agris.fao.org/search/en/providers/123819/records/647357fc08fd68d54601590e.
55. Murphy, B. P. & Russell-Smith, J. (2010). Fire severity in a northern Australian savanna landscape: The importance of time since previous fire. International Journal of Wildland Fire, 19(1), 46-51.
56. Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481-486. https://doi.org/10.1109/TGRS.1995.8746029.
57. Olson, J. S. (1963). Energy storage and the balance of producers and decomposers in ecological systems. Ecology, 44(2), 322-331. https://doi.org/10.2307/1932179.
58. Ottmar, R. D., Sandberg, D. V., Riccardi, C. L. & Prichard, S. J. (2007). An overview of the fuel characteristic classification system quantifying, classifying, and creating fuelbeds for resource planning. Canadian Journal of Forest Research, 37(12), 2383-2393. https://doi.org/10.1139/X07-077.
59. Özcan, Z., Caglayan, İ. & Kabak, Ö. (2024). A comprehensive taxonomy for forest fire risk assessment: Bridging methodological gaps and proposing future directions. Environmental Monitoring and Assessment, 196(9), 825. https://doi.org/10.1007/s10661-024-12982-8.
60. Özcanlı, M. & Yilmaz, E. (2024). Türkiye’de arazi örtüsü/kullanımı değişimlerinin belirlenmesi ve iklim değişimine olası etkileri. Türk Coğrafya Dergisi, 86, 7-24. https://doi.org/10.17211/tcd.1408186.
61. Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., Nabuurs, G.-J., Shvidenko, A., Hashimoto, S., Lerink, B., Schepaschenko, D., Castanho, A. & Murdiyarso, D. (2024). The enduring world forest carbon sink. Nature, 631(8021), 563-569. https://doi.org/10.1038/s41586-024-07602-x.
62. Pausas, J. G. & Keeley, J. E. (2019). Wildfires as an ecosystem service. Frontiers in Ecology and the Environment, 17(5), 289-295. https://doi.org/10.1002/fee.2044.
63. Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J. & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503-510. https://doi.org/10.1016/j.tree.2005.05.011.
64. Psistaki, K., Tsantopoulos, G. & Paschalidou, A. K. (2024). An overview of the role of forests in climate change mitigation. Sustainability, 16(14), 6089. https://doi.org/10.3390/su16146089.
65. Puletti, N., Chianucci, F. & Castaldi, C. (2018). Use of Sentinel-2 for forest classification in Mediterranean environments. Annals of Silvicultural Research, 42(1), 32-38. https://doi.org/10.12899/asr-1463.
66. Reich, R. M., Lundquist, J. E. & Bravo, V. A. (2004). Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire, 13(1), 119-129.
67. Rodrigo, A., Retana, J. & Picó, F. X. (2004). Direct regeneration is not the only response of Mediterranean forests to large fires. Ecology, 85(3), 716-729.
68. Rollins, M. G., Keane, R. E. & Parsons, R. A. (2004). Mapping fuels and fire regimes using remote sensing, ecosystem simulation, and gradient modeling. Ecological Applications, 14(1), 75-95. https://doi.org/10.1890/02-5145.
69. Rothermel, R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. Intermountain Forest & Range Experiment Station, Forest Service, U.S. Department of Agriculture.
70. Saatchi, S., Halligan, K., Despain, D. G. & Crabtree, R. L. (2007). Estimation of forest fuel load from radar remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1726-1740. https://doi.org/10.1109/TGRS.2006.887002.
71. Santos, S. M. B., Bento-Gonçalves, A., Franca-Rocha, W. & Baptista, G. (2020). Assessment of burned forest area severity and postfire regrowth in Chapada Diamantina National Park (Bahia, Brazil) using dNBR and RdNBR spectral indices. Geosciences, 10(3), 106. https://doi.org/10.3390/geosciences10030106.
72. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. (2001). Catastrophic shifts in ecosystems. Nature, 413(6856), 591-596. https://doi.org/10.1038/35098000.
73. Scott, J. H. & Reinhardt, E. D. (2001). Assessing crown fire potential by linking models of surface and crown fire behavior (No. 29). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
74. Sivrikaya, F., Günlü, A., Küçük, Ö. & Ürker, O. (2024). Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices. Ecological Informatics, 79, 102461. https://doi.org/10.1016/j.ecoinf.2024.102461.
75. Tavşanoğlu, Ç. & Gürkan, B. (2004). Akdeniz havzasında bitkilerin kuraklık ve yangına uyumları, OT Sistematik Botanik Dergisi, 11(1), 119-132.
76. Van Der Heijden, M. G. A., Bardgett, R. D. & Van Straalen, N. M. (2008). The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters, 11(3), 296-310. https://doi.org/10.1111/j.1461-0248.2007.01139.x.
77. Viegas, D. X., Soares, J. & Almeida, M. (2013). Combustibility of a mixture of live and dead fuel components. International Journal of Wildland Fire, 22(7), 992-1002. https://doi.org/10.1071/WF12031.
78. Wagner, C. E. V. (1977). Conditions for the start and spread of crown fire. Canadian Journal of Forest Research, 7(1), 23-34. https://doi.org/10.1139/x77-004.
79. Wells, A. G., Munson, S. M., Sesnie, S. E. & Villarreal, M. L. (2021). Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid Grassland. Fire, 4(4), 84. https://doi.org/10.3390/fire4040084.
80. Xulu, S., Mbatha, N. & Peerbhay, K. (2021). Burned area mapping over the Southern Cape Forestry Region, South Africa using Sentinel data within GEE cloud platform. ISPRS International Journal of Geo-Information, 10(8), 511. https://doi.org/10.3390/ijgi10080511.
81. Yebra, M., Quan, X., Riaño, D., Rozas Larraondo, P., van Dijk, A. I. J. M. & Cary, G. J. (2018). A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sensing of Environment, 212, 260-272. https://doi.org/10.1016/j.rse.2018.04.053.